In video conferencing, image quality heavily depends on the lighting conditions of the environment. Unsatisfactory environmental lighting often results in under exposure and low contrast in the areas of interest. This is particularly true when attempting to capture a participant's face. Prior attempts to resolve this issue have been based on such things as creating an intensity transformation model on pixels based on the detection of a human face. These face centric approaches may still suffer from bad illumination conditions as the face detection often relies on a reasonably good observation of skin tone pixels. Moreover, in circumstances where the area of interest is not a human face (e.g., objects that people hold close to the camera during a desk top video conferencing session) a face centric approach will fail to adjust the illumination properly.